Triple
T2944473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denton County Commissioners Court |
E79466
|
entity |
| Predicate | numberOfCommissionerPrecincts |
P21525
|
FINISHED |
| Object | 4 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 4 | Statement: [Denton County Commissioners Court, numberOfCommissionerPrecincts, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCommissionerPrecincts Context triple: [Denton County Commissioners Court, numberOfCommissionerPrecincts, 4]
-
A.
numberOfCommissioners
chosen
Indicates the specific count of commissioners associated with a given entity or context.
-
B.
numberOfResidentCommissioners
Indicates the quantity of resident commissioners associated with a given entity.
-
C.
hasCountyCommissioners
Indicates that an entity is governed or overseen by one or more county commissioners.
-
D.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
E.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad8b1089588190b74d9e2505e45762 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98b0db5081908e84def20a5e4a2d |
completed | March 8, 2026, 3:41 p.m. |
| PD | Predicate disambiguation | batch_69ad96088fb481909976b436c2b729d9 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:56 p.m.